RecSysWiki - User contributions [en]

LDOS Comoda

2014-07-08T14:55:09Z

Markotka:

The LDOS-CoMoDa is a dataset of movie ratings with users (ratters) context recorded. It contains twelve contextual variables that are potentially relevant in the process of movie selection.

It can be obtained here: [http://www.ldos.si/comoda.html].

The description:
<pre>
LDOS - CoMoDa dataset

______________________
Data fields:
versionDate: date of the dataset version
userID (15 - 200, some missing)
itemID (1 -4138, some missing)
rating (1-5)
user's age
user's sex (1=male, 2= female)
user's city
user's country
time (1-4)
daytype (1-3)
season (1-4)
location (1-3)
weather (1-5)
social (1-7)
endEmo(1-7)
dominantEmo (1-7)
mood (1-3)
physical (1-2)
decision (1-2)
interaction (1-2)
movie director
movie's country
movie's language
movie's year
genre1
genre2
genre3
actor1
actor2
actor3
movie's budget

_______________________
Context variables:
time : Morning, Afternoon, Evening, Night
daytype : Working day, Weekend, Holiday
season : Spring, Summer, Autumn, Winter
location : Home, Public place, Friend's house
weather : Sunny / clear, Rainy, Stormy, Snowy, Cloudy
social : Alone, My partner, Friends, Colleagues, Parents, Public, My family
endEmo : Sad, Happy, Scared, Surprised, Angry, Disgusted, Neutral
dominantEmo : Sad, Happy, Scared, Surprised, Angry, Disgusted, Neutral
mood : Positive, Neutral, Negative
physical : Healthy, Ill
decision : User decided which movie to watch, User was given a movie
interaction : first interaction with a movie, n-th interaction with a movie

Context values in the database corespond to this order.
(for example: daytype-> 1 = Working day, 2 = Weekend, 3 = Holiday)
_______________________
Missing value:
-1
</pre>

[[Category: Dataset]]
[[Category: Movie recommendation]]

LDOS Comoda

2014-07-08T14:53:27Z

Markotka:

The LDOS-CoMoDa is a dataset of movie ratings with users (ratters) context recorded. It contains twelve contextual variables that are potentially relevant in the process of movie selection.

It can be obtained [http://www.ldos.si/comoda.html|here].

The description:
<pre>
LDOS - CoMoDa dataset

______________________
Data fields:
versionDate: date of the dataset version
userID (15 - 200, some missing)
itemID (1 -4138, some missing)
rating (1-5)
user's age
user's sex (1=male, 2= female)
user's city
user's country
time (1-4)
daytype (1-3)
season (1-4)
location (1-3)
weather (1-5)
social (1-7)
endEmo(1-7)
dominantEmo (1-7)
mood (1-3)
physical (1-2)
decision (1-2)
interaction (1-2)
movie director
movie's country
movie's language
movie's year
genre1
genre2
genre3
actor1
actor2
actor3
movie's budget

_______________________
Context variables:
time : Morning, Afternoon, Evening, Night
daytype : Working day, Weekend, Holiday
season : Spring, Summer, Autumn, Winter
location : Home, Public place, Friend's house
weather : Sunny / clear, Rainy, Stormy, Snowy, Cloudy
social : Alone, My partner, Friends, Colleagues, Parents, Public, My family
endEmo : Sad, Happy, Scared, Surprised, Angry, Disgusted, Neutral
dominantEmo : Sad, Happy, Scared, Surprised, Angry, Disgusted, Neutral
mood : Positive, Neutral, Negative
physical : Healthy, Ill
decision : User decided which movie to watch, User was given a movie
interaction : first interaction with a movie, n-th interaction with a movie

Context values in the database corespond to this order.
(for example: daytype-> 1 = Working day, 2 = Weekend, 3 = Holiday)
_______________________
Missing value:
-1
</pre>

[[Category: Dataset]]
[[Category: Movie recommendation]]

Empire 2014

2014-02-17T17:37:49Z

Markotka: /* PROGRAMME COMMITTEE */

[[Category:Workshop]]
EMPIRE 2014 - 2nd workshop on "Emotions and Personality in Personalized Services" http://empire2014.wordpress.com

in conjuction with UMAP 2014 (Aalborg, Denmark)
http://www.um.org/umap2014/

== ABSTRACT ==

The 2nd workshop on Emotions and Personality in Personalized Services will be organized in conjunction with the UMAP 2014 conference and will be held in Aalborg, Denmark.

Personality and emotions shape our daily lives by having a strong influence on our preferences, decisions and behaviour in general. Hence, personalized systems that want to adapt to end users need to be aware of the user’s personality and/or emotions to perform well. Affective factors may include longterm personality traits or shorterterm states ranging from ‘affect dispositions’, ‘attitudes’ (liking, loving, hating,…), ‘interpersonal stances’ (distant, cold, warm,…), ‘moods’ (cheerful, irritable, depressed,…) or ‘real emotions’.

Recently, there have been extensive studies on the role of personality on user preferences, gaming styles and learning styles. Furthermore, some studies showed that it is possible to extract personality information about a user without annoying questionnaires, by analyzing the publicly available user’s social media feeds. Also, the affective computing community has developed sophisticated techniques that allow for accurate and unobtrusive emotion detection. Generally, emotions can be used in personalized systems in two ways: (i) either to change the emotion (or mood, e.g. from a negative to a positive) or (ii) to sustain the current emotion (e.g. keep a user “charged” while doing sports). Recent studies showed that such information can be used in various personalized systems like emotionaware recommender systems.

The workshop will accept papers that discuss (i) aspects of personality and emotions acquisition (especially implicit methods, e.g. from social media), (ii) user modeling, (iii) adaptation strategies (e.g. to different learning styles, openness to diverse content etc.), (iv) scenarios/domains where emotions and personalities could be utilized effectively, (v) corpora, (vi) privacy issues, (vii) evaluation measures/strategies and other related topics.

== TOPICS ==

* Adaptation strategies using affect and/or personality (e.g. to different learning styles, openness to diverse content etc.)
* Scenarios/domains where emotions and personality could be utilized effectively
* Privacy issues
* Evaluation measures/strategies
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction…)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Datasets for affective modeling (collecting, available)
* Personality traits acquisition (explicit and implicit)
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits
* Personality and users’ tasks/goals
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Detecting triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors
* Personality-based preference elicitation

== SUBMISSION INSTRUCTIONS ==

We accept two kinds of submissions: (i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2014

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a CEUR-WS volume.

== SPINOFF PUBLICATION ==

Authors may wish to consider submitting thoroughly extended versions of their manuscripts to the UMUAI Special Issue on Personality in Personalized Services ( http://www.umuai.org/news_on_journal.html ).

Further information can be found on the workshop’s web page http://empire2014.wordpress.com

== IMPORTANT DATES ==

* April, 1, 2014 Paper submission deadline
* May, 1, 2014 Notification of acceptance
* To Be Announced Workshop day

== ORGANIZING COMMITTEE ==
* Marko Tkalčič, Johannes Kepler University Linz, Austria / University of Ljubljana, Slovenia
* Berardina De Carolis, University of Bari Aldo Moro, Italy
* Marco de Gemmis, University of Bari Aldo Moro, Italy
* Ante Odić, Outfit7 (Slovenian subsidiary Ekipa2 d.o.o.), Ljubljana, Slovenia
* Andrej Košir, University of Ljubljana, Slovenia

== PROGRAMME COMMITTEE ==
* Aleksander Valjamae, Linköping University, Sweden
* Alessandro Vinciarelli, Glasgow University, UK
* Floriana Grasso, Liverpool University, UK
* Francesco Ricci, Free University of Bolzano, Italy
* Giovanni Semeraro, Universita di Bari, Italy
* Ioannis Arapakis, Yahoo! BCN, Spain
* Ivan Cantador, Universidad Autónoma de Madrid, Spain
* Li Chen, Hong Kong Batist University
* Luca Chittaro, University of Udine, Italy
* Man Kwan Shan, National Chengchi University, China
* Maria Nunes, University of Sergipe, Brazil
* Matt Dennis, University of Aberdeen, UK
* Mehdi Elahi, Free University of Bolzano, Italy
* Mohammad Soleymani, Imperial College, UK
* Neal Lathia, Cambridge University, UK
* Olga Santos, UNED, Spain
* Oliver Brdizcka, PARC, USA
* Pasquale Lops, Universita di Bari, Italy
* Pearl Pu, EPFL, Switzerland
* Rong Hu, EPFL, Switzerland
* Sabine Graf, Athabasca University, Canada
* Stephen Fairclough, Liverpool John Moore University, UK
* Viviana Patti, University of Torino, Italy
* Yi-Hsuan (Eric) Yang, Academia Sinica, Taipei

Empire 2014

2014-02-17T17:36:59Z

Markotka: /* ORGANIZING COMMITTEE */

[[Category:Workshop]]
EMPIRE 2014 - 2nd workshop on "Emotions and Personality in Personalized Services" http://empire2014.wordpress.com

in conjuction with UMAP 2014 (Aalborg, Denmark)
http://www.um.org/umap2014/

== ABSTRACT ==

The 2nd workshop on Emotions and Personality in Personalized Services will be organized in conjunction with the UMAP 2014 conference and will be held in Aalborg, Denmark.

Personality and emotions shape our daily lives by having a strong influence on our preferences, decisions and behaviour in general. Hence, personalized systems that want to adapt to end users need to be aware of the user’s personality and/or emotions to perform well. Affective factors may include longterm personality traits or shorterterm states ranging from ‘affect dispositions’, ‘attitudes’ (liking, loving, hating,…), ‘interpersonal stances’ (distant, cold, warm,…), ‘moods’ (cheerful, irritable, depressed,…) or ‘real emotions’.

Recently, there have been extensive studies on the role of personality on user preferences, gaming styles and learning styles. Furthermore, some studies showed that it is possible to extract personality information about a user without annoying questionnaires, by analyzing the publicly available user’s social media feeds. Also, the affective computing community has developed sophisticated techniques that allow for accurate and unobtrusive emotion detection. Generally, emotions can be used in personalized systems in two ways: (i) either to change the emotion (or mood, e.g. from a negative to a positive) or (ii) to sustain the current emotion (e.g. keep a user “charged” while doing sports). Recent studies showed that such information can be used in various personalized systems like emotionaware recommender systems.

The workshop will accept papers that discuss (i) aspects of personality and emotions acquisition (especially implicit methods, e.g. from social media), (ii) user modeling, (iii) adaptation strategies (e.g. to different learning styles, openness to diverse content etc.), (iv) scenarios/domains where emotions and personalities could be utilized effectively, (v) corpora, (vi) privacy issues, (vii) evaluation measures/strategies and other related topics.

== TOPICS ==

* Adaptation strategies using affect and/or personality (e.g. to different learning styles, openness to diverse content etc.)
* Scenarios/domains where emotions and personality could be utilized effectively
* Privacy issues
* Evaluation measures/strategies
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction…)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Datasets for affective modeling (collecting, available)
* Personality traits acquisition (explicit and implicit)
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits
* Personality and users’ tasks/goals
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Detecting triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors
* Personality-based preference elicitation

== SUBMISSION INSTRUCTIONS ==

We accept two kinds of submissions: (i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2014

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a CEUR-WS volume.

== SPINOFF PUBLICATION ==

Authors may wish to consider submitting thoroughly extended versions of their manuscripts to the UMUAI Special Issue on Personality in Personalized Services ( http://www.umuai.org/news_on_journal.html ).

Further information can be found on the workshop’s web page http://empire2014.wordpress.com

== IMPORTANT DATES ==

* April, 1, 2014 Paper submission deadline
* May, 1, 2014 Notification of acceptance
* To Be Announced Workshop day

== ORGANIZING COMMITTEE ==
* Marko Tkalčič, Johannes Kepler University Linz, Austria / University of Ljubljana, Slovenia
* Berardina De Carolis, University of Bari Aldo Moro, Italy
* Marco de Gemmis, University of Bari Aldo Moro, Italy
* Ante Odić, Outfit7 (Slovenian subsidiary Ekipa2 d.o.o.), Ljubljana, Slovenia
* Andrej Košir, University of Ljubljana, Slovenia

== PROGRAMME COMMITTEE ==
Aleksander Valjamae, Linköping University, Sweden
Alessandro Vinciarelli, Glasgow University, UK
Floriana Grasso, Liverpool University, UK
Francesco Ricci, Free University of Bolzano, Italy
Giovanni Semeraro, Universita di Bari, Italy
Ioannis Arapakis, Yahoo! BCN, Spain
Ivan Cantador, Universidad Autónoma de Madrid, Spain
Li Chen, Hong Kong Batist University
Luca Chittaro, University of Udine, Italy
Man Kwan Shan, National Chengchi University, China
Maria Nunes, University of Sergipe, Brazil
Matt Dennis, University of Aberdeen, UK
Mehdi Elahi, Free University of Bolzano, Italy
Mohammad Soleymani, Imperial College, UK
Neal Lathia, Cambridge University, UK
Olga Santos, UNED, Spain
Oliver Brdizcka, PARC, USA
Pasquale Lops, Universita di Bari, Italy
Pearl Pu, EPFL, Switzerland
Rong Hu, EPFL, Switzerland
Sabine Graf, Athabasca University, Canada
Stephen Fairclough, Liverpool John Moore University, UK
Viviana Patti, University of Torino, Italy
Yi-Hsuan (Eric) Yang, Academia Sinica, Taipei

Empire 2014

2014-02-17T17:36:35Z

Markotka: /* IMPORTANT DATES */

[[Category:Workshop]]
EMPIRE 2014 - 2nd workshop on "Emotions and Personality in Personalized Services" http://empire2014.wordpress.com

in conjuction with UMAP 2014 (Aalborg, Denmark)
http://www.um.org/umap2014/

== ABSTRACT ==

The 2nd workshop on Emotions and Personality in Personalized Services will be organized in conjunction with the UMAP 2014 conference and will be held in Aalborg, Denmark.

Personality and emotions shape our daily lives by having a strong influence on our preferences, decisions and behaviour in general. Hence, personalized systems that want to adapt to end users need to be aware of the user’s personality and/or emotions to perform well. Affective factors may include longterm personality traits or shorterterm states ranging from ‘affect dispositions’, ‘attitudes’ (liking, loving, hating,…), ‘interpersonal stances’ (distant, cold, warm,…), ‘moods’ (cheerful, irritable, depressed,…) or ‘real emotions’.

Recently, there have been extensive studies on the role of personality on user preferences, gaming styles and learning styles. Furthermore, some studies showed that it is possible to extract personality information about a user without annoying questionnaires, by analyzing the publicly available user’s social media feeds. Also, the affective computing community has developed sophisticated techniques that allow for accurate and unobtrusive emotion detection. Generally, emotions can be used in personalized systems in two ways: (i) either to change the emotion (or mood, e.g. from a negative to a positive) or (ii) to sustain the current emotion (e.g. keep a user “charged” while doing sports). Recent studies showed that such information can be used in various personalized systems like emotionaware recommender systems.

The workshop will accept papers that discuss (i) aspects of personality and emotions acquisition (especially implicit methods, e.g. from social media), (ii) user modeling, (iii) adaptation strategies (e.g. to different learning styles, openness to diverse content etc.), (iv) scenarios/domains where emotions and personalities could be utilized effectively, (v) corpora, (vi) privacy issues, (vii) evaluation measures/strategies and other related topics.

== TOPICS ==

* Adaptation strategies using affect and/or personality (e.g. to different learning styles, openness to diverse content etc.)
* Scenarios/domains where emotions and personality could be utilized effectively
* Privacy issues
* Evaluation measures/strategies
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction…)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Datasets for affective modeling (collecting, available)
* Personality traits acquisition (explicit and implicit)
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits
* Personality and users’ tasks/goals
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Detecting triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors
* Personality-based preference elicitation

== SUBMISSION INSTRUCTIONS ==

We accept two kinds of submissions: (i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2014

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a CEUR-WS volume.

== SPINOFF PUBLICATION ==

Authors may wish to consider submitting thoroughly extended versions of their manuscripts to the UMUAI Special Issue on Personality in Personalized Services ( http://www.umuai.org/news_on_journal.html ).

Further information can be found on the workshop’s web page http://empire2014.wordpress.com

== IMPORTANT DATES ==

* April, 1, 2014 Paper submission deadline
* May, 1, 2014 Notification of acceptance
* To Be Announced Workshop day

== ORGANIZING COMMITTEE ==
Marko Tkalčič, Johannes Kepler University Linz, Austria / University of Ljubljana, Slovenia
Berardina De Carolis, University of Bari Aldo Moro, Italy
Marco de Gemmis, University of Bari Aldo Moro, Italy
Ante Odić, Outfit7 (Slovenian subsidiary Ekipa2 d.o.o.), Ljubljana, Slovenia
Andrej Košir, University of Ljubljana, Slovenia

== PROGRAMME COMMITTEE ==
Aleksander Valjamae, Linköping University, Sweden
Alessandro Vinciarelli, Glasgow University, UK
Floriana Grasso, Liverpool University, UK
Francesco Ricci, Free University of Bolzano, Italy
Giovanni Semeraro, Universita di Bari, Italy
Ioannis Arapakis, Yahoo! BCN, Spain
Ivan Cantador, Universidad Autónoma de Madrid, Spain
Li Chen, Hong Kong Batist University
Luca Chittaro, University of Udine, Italy
Man Kwan Shan, National Chengchi University, China
Maria Nunes, University of Sergipe, Brazil
Matt Dennis, University of Aberdeen, UK
Mehdi Elahi, Free University of Bolzano, Italy
Mohammad Soleymani, Imperial College, UK
Neal Lathia, Cambridge University, UK
Olga Santos, UNED, Spain
Oliver Brdizcka, PARC, USA
Pasquale Lops, Universita di Bari, Italy
Pearl Pu, EPFL, Switzerland
Rong Hu, EPFL, Switzerland
Sabine Graf, Athabasca University, Canada
Stephen Fairclough, Liverpool John Moore University, UK
Viviana Patti, University of Torino, Italy
Yi-Hsuan (Eric) Yang, Academia Sinica, Taipei

Empire 2014

2014-02-17T17:35:41Z

Markotka: /* TOPICS */

[[Category:Workshop]]
EMPIRE 2014 - 2nd workshop on "Emotions and Personality in Personalized Services" http://empire2014.wordpress.com

in conjuction with UMAP 2014 (Aalborg, Denmark)
http://www.um.org/umap2014/

== ABSTRACT ==

The 2nd workshop on Emotions and Personality in Personalized Services will be organized in conjunction with the UMAP 2014 conference and will be held in Aalborg, Denmark.

Personality and emotions shape our daily lives by having a strong influence on our preferences, decisions and behaviour in general. Hence, personalized systems that want to adapt to end users need to be aware of the user’s personality and/or emotions to perform well. Affective factors may include longterm personality traits or shorterterm states ranging from ‘affect dispositions’, ‘attitudes’ (liking, loving, hating,…), ‘interpersonal stances’ (distant, cold, warm,…), ‘moods’ (cheerful, irritable, depressed,…) or ‘real emotions’.

Recently, there have been extensive studies on the role of personality on user preferences, gaming styles and learning styles. Furthermore, some studies showed that it is possible to extract personality information about a user without annoying questionnaires, by analyzing the publicly available user’s social media feeds. Also, the affective computing community has developed sophisticated techniques that allow for accurate and unobtrusive emotion detection. Generally, emotions can be used in personalized systems in two ways: (i) either to change the emotion (or mood, e.g. from a negative to a positive) or (ii) to sustain the current emotion (e.g. keep a user “charged” while doing sports). Recent studies showed that such information can be used in various personalized systems like emotionaware recommender systems.

The workshop will accept papers that discuss (i) aspects of personality and emotions acquisition (especially implicit methods, e.g. from social media), (ii) user modeling, (iii) adaptation strategies (e.g. to different learning styles, openness to diverse content etc.), (iv) scenarios/domains where emotions and personalities could be utilized effectively, (v) corpora, (vi) privacy issues, (vii) evaluation measures/strategies and other related topics.

== TOPICS ==

* Adaptation strategies using affect and/or personality (e.g. to different learning styles, openness to diverse content etc.)
* Scenarios/domains where emotions and personality could be utilized effectively
* Privacy issues
* Evaluation measures/strategies
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction…)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Datasets for affective modeling (collecting, available)
* Personality traits acquisition (explicit and implicit)
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits
* Personality and users’ tasks/goals
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Detecting triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors
* Personality-based preference elicitation

== SUBMISSION INSTRUCTIONS ==

We accept two kinds of submissions: (i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2014

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a CEUR-WS volume.

== SPINOFF PUBLICATION ==

Authors may wish to consider submitting thoroughly extended versions of their manuscripts to the UMUAI Special Issue on Personality in Personalized Services ( http://www.umuai.org/news_on_journal.html ).

Further information can be found on the workshop’s web page http://empire2014.wordpress.com

== IMPORTANT DATES ==

April, 1, 2014 Paper submission deadline
May, 1, 2014 Notification of acceptance
To Be Announced Workshop day

== ORGANIZING COMMITTEE ==
Marko Tkalčič, Johannes Kepler University Linz, Austria / University of Ljubljana, Slovenia
Berardina De Carolis, University of Bari Aldo Moro, Italy
Marco de Gemmis, University of Bari Aldo Moro, Italy
Ante Odić, Outfit7 (Slovenian subsidiary Ekipa2 d.o.o.), Ljubljana, Slovenia
Andrej Košir, University of Ljubljana, Slovenia

== PROGRAMME COMMITTEE ==
Aleksander Valjamae, Linköping University, Sweden
Alessandro Vinciarelli, Glasgow University, UK
Floriana Grasso, Liverpool University, UK
Francesco Ricci, Free University of Bolzano, Italy
Giovanni Semeraro, Universita di Bari, Italy
Ioannis Arapakis, Yahoo! BCN, Spain
Ivan Cantador, Universidad Autónoma de Madrid, Spain
Li Chen, Hong Kong Batist University
Luca Chittaro, University of Udine, Italy
Man Kwan Shan, National Chengchi University, China
Maria Nunes, University of Sergipe, Brazil
Matt Dennis, University of Aberdeen, UK
Mehdi Elahi, Free University of Bolzano, Italy
Mohammad Soleymani, Imperial College, UK
Neal Lathia, Cambridge University, UK
Olga Santos, UNED, Spain
Oliver Brdizcka, PARC, USA
Pasquale Lops, Universita di Bari, Italy
Pearl Pu, EPFL, Switzerland
Rong Hu, EPFL, Switzerland
Sabine Graf, Athabasca University, Canada
Stephen Fairclough, Liverpool John Moore University, UK
Viviana Patti, University of Torino, Italy
Yi-Hsuan (Eric) Yang, Academia Sinica, Taipei

Empire 2014

2014-02-17T17:34:21Z

Markotka: Created page with "Category:Workshop EMPIRE 2014 - 2nd workshop on "Emotions and Personality in Personalized Services" http://empire2014.wordpress.com in conjuction with UMAP 2014 (Aalborg,..."

[[Category:Workshop]]
EMPIRE 2014 - 2nd workshop on "Emotions and Personality in Personalized Services" http://empire2014.wordpress.com

in conjuction with UMAP 2014 (Aalborg, Denmark)
http://www.um.org/umap2014/

== ABSTRACT ==

The 2nd workshop on Emotions and Personality in Personalized Services will be organized in conjunction with the UMAP 2014 conference and will be held in Aalborg, Denmark.

Personality and emotions shape our daily lives by having a strong influence on our preferences, decisions and behaviour in general. Hence, personalized systems that want to adapt to end users need to be aware of the user’s personality and/or emotions to perform well. Affective factors may include longterm personality traits or shorterterm states ranging from ‘affect dispositions’, ‘attitudes’ (liking, loving, hating,…), ‘interpersonal stances’ (distant, cold, warm,…), ‘moods’ (cheerful, irritable, depressed,…) or ‘real emotions’.

Recently, there have been extensive studies on the role of personality on user preferences, gaming styles and learning styles. Furthermore, some studies showed that it is possible to extract personality information about a user without annoying questionnaires, by analyzing the publicly available user’s social media feeds. Also, the affective computing community has developed sophisticated techniques that allow for accurate and unobtrusive emotion detection. Generally, emotions can be used in personalized systems in two ways: (i) either to change the emotion (or mood, e.g. from a negative to a positive) or (ii) to sustain the current emotion (e.g. keep a user “charged” while doing sports). Recent studies showed that such information can be used in various personalized systems like emotionaware recommender systems.

The workshop will accept papers that discuss (i) aspects of personality and emotions acquisition (especially implicit methods, e.g. from social media), (ii) user modeling, (iii) adaptation strategies (e.g. to different learning styles, openness to diverse content etc.), (iv) scenarios/domains where emotions and personalities could be utilized effectively, (v) corpora, (vi) privacy issues, (vii) evaluation measures/strategies and other related topics.

== TOPICS ==

- Adaptation strategies using affect and/or personality (e.g. to different learning styles, openness to diverse content etc.)
- Scenarios/domains where emotions and personality could be utilized effectively
- Privacy issues
- Evaluation measures/strategies
- Emotions as context
- Emotions in the decision-making process for recommender systems
- Role of personality on user similarities
- Emotion detection in recommended content consumption
- Emotion detection as non-invasive feedback
- Affective tagging of multimedia content and services
- Emotion-based evaluation metrics (satisfaction…)
- Lifestyle recommender systems
- Personality and mood for group decision making
- Incorporating personality and emotions in user models
- Datasets for affective modeling (collecting, available)
- Personality traits acquisition (explicit and implicit)
- Personality and interfaces/control/bubble-control
- Could interfaces/control/bubble-control be personalized based on personality traits
- Personality and users’ tasks/goals
- Social signal processing for personalized services
- Strategies for modeling emotions and personality
- Detecting triggers and causes of emotion
- Theories about the relationship between reasoning and affect, between decision-making and affect
- Methods for evaluating the utility of adaptation to affective factors
- Personality-based preference elicitation

== SUBMISSION INSTRUCTIONS ==

We accept two kinds of submissions: (i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2014

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a CEUR-WS volume.

== SPINOFF PUBLICATION ==

Authors may wish to consider submitting thoroughly extended versions of their manuscripts to the UMUAI Special Issue on Personality in Personalized Services ( http://www.umuai.org/news_on_journal.html ).

Further information can be found on the workshop’s web page http://empire2014.wordpress.com

== IMPORTANT DATES ==

April, 1, 2014 Paper submission deadline
May, 1, 2014 Notification of acceptance
To Be Announced Workshop day

== ORGANIZING COMMITTEE ==
Marko Tkalčič, Johannes Kepler University Linz, Austria / University of Ljubljana, Slovenia
Berardina De Carolis, University of Bari Aldo Moro, Italy
Marco de Gemmis, University of Bari Aldo Moro, Italy
Ante Odić, Outfit7 (Slovenian subsidiary Ekipa2 d.o.o.), Ljubljana, Slovenia
Andrej Košir, University of Ljubljana, Slovenia

== PROGRAMME COMMITTEE ==
Aleksander Valjamae, Linköping University, Sweden
Alessandro Vinciarelli, Glasgow University, UK
Floriana Grasso, Liverpool University, UK
Francesco Ricci, Free University of Bolzano, Italy
Giovanni Semeraro, Universita di Bari, Italy
Ioannis Arapakis, Yahoo! BCN, Spain
Ivan Cantador, Universidad Autónoma de Madrid, Spain
Li Chen, Hong Kong Batist University
Luca Chittaro, University of Udine, Italy
Man Kwan Shan, National Chengchi University, China
Maria Nunes, University of Sergipe, Brazil
Matt Dennis, University of Aberdeen, UK
Mehdi Elahi, Free University of Bolzano, Italy
Mohammad Soleymani, Imperial College, UK
Neal Lathia, Cambridge University, UK
Olga Santos, UNED, Spain
Oliver Brdizcka, PARC, USA
Pasquale Lops, Universita di Bari, Italy
Pearl Pu, EPFL, Switzerland
Rong Hu, EPFL, Switzerland
Sabine Graf, Athabasca University, Canada
Stephen Fairclough, Liverpool John Moore University, UK
Viviana Patti, University of Torino, Italy
Yi-Hsuan (Eric) Yang, Academia Sinica, Taipei

LDOS PerAff 1

2013-10-24T12:29:26Z

Markotka:

The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli and ratings. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions.

Also refer to the paper Tkalčič, Košir, Tasič, ''The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata'' in the Journal on Multimodal User Interfaces, March 2013, Volume 7, Issue 1-2, pp 143-155

== Excerpt from the dataset ==
<pre>
user_id image_idtag genre wt wt_m val_m val_sd ar_m ar_sd dom_m dom_sd B_1 B_2 B_3 B_4 B_5 gender age rating
10 6910 Bomber weapon 2614 5307 5,31 2,28 5,62 2,46 5,1 2,46 3,2 2,7 2,9 3,5 2,9 0 18 4
10 9331 Assault violence 2240 3214 2,03 1,35 6,04 2,35 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
10 7052 HairDryer still 2223 2665 4,93 0,81 2,75 1,8 5,82 1,93 3,2 2,7 2,9 3,5 2,9 0 18 1
10 1280 Rat animal 1906 3093 3,66 1,75 4,93 2,01 5,05 2,2 3,2 2,7 2,9 3,5 2,9 0 18 1
10 2394 Medicalworker people 1943 2993 5,76 1,74 3,89 2,26 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
</pre>

== External links ==
* http://slavnik.fe.uni-lj.si/markot/Main/LDOS-PerAff-1
* http://dx.doi.org/10.1007/s12193-012-0107-7

[[Category:Dataset]]

LDOS PerAff 1

2013-10-24T12:28:50Z

Markotka:

LDOS PerAff 1

2013-10-24T12:27:48Z

Markotka:

The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli and ratings. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions.

Also refer to the paper Tkalčič, Košir, Tasič, ''The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata'' in the Journal on Multimodal User Interfaces, March 2013, Volume 7, Issue 1-2, pp 143-155

== Excerpt from the dataset ==
<pre>
user_id image_idtag genre watch_time wt_m valence_m valence_sd arousal_m arousal_sd dominance_m dominance_sd B_1 B_2 B_3 B_4 B_5 gender age rating
10 6910 Bomber weapon 2614 5307 5,31 2,28 5,62 2,46 5,1 2,46 3,2 2,7 2,9 3,5 2,9 0 18 4
10 9331 Assault violence 2240 3214 2,03 1,35 6,04 2,35 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
10 7052 HairDryer still 2223 2665 4,93 0,81 2,75 1,8 5,82 1,93 3,2 2,7 2,9 3,5 2,9 0 18 1
10 1280 Rat animal 1906 3093 3,66 1,75 4,93 2,01 5,05 2,2 3,2 2,7 2,9 3,5 2,9 0 18 1
10 2394 Medicalworker people 1943 2993 5,76 1,74 3,89 2,26 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
</pre>

== External links ==
* http://slavnik.fe.uni-lj.si/markot/Main/LDOS-PerAff-1
* http://dx.doi.org/10.1007/s12193-012-0107-7

[Category:Dataset]

LDOS PerAff 1

2013-10-24T12:27:11Z

Markotka:

The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli and ratings. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions.

Also refer to the paper Tkalčič, Košir, Tasič, ''The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata'' in the Journal on Multimodal User Interfaces, March 2013, Volume 7, Issue 1-2, pp 143-155

== Excerpt from the dataset ==
<pre>
user_id image_idimage_tag genre watching_time wt_m valence_m valence_sd arousal_m arousal_sd dominance_m dominance_sd B_1 B_2 B_3 B_4 B_5 gender age rating
10 6910 Bomber weapon 2614 5307 5,31 2,28 5,62 2,46 5,1 2,46 3,2 2,7 2,9 3,5 2,9 0 18 4
10 9331 Assault violence 2240 3214 2,03 1,35 6,04 2,35 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
10 7052 HairDryer still 2223 2665 4,93 0,81 2,75 1,8 5,82 1,93 3,2 2,7 2,9 3,5 2,9 0 18 1
10 1280 Rat animal 1906 3093 3,66 1,75 4,93 2,01 5,05 2,2 3,2 2,7 2,9 3,5 2,9 0 18 1
10 2394 Medicalworker people 1943 2993 5,76 1,74 3,89 2,26 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
</pre>

== External links ==
* http://slavnik.fe.uni-lj.si/markot/Main/LDOS-PerAff-1
* http://dx.doi.org/10.1007/s12193-012-0107-7

[Category:Dataset]

LDOS PerAff 1

2013-10-24T12:26:08Z

Markotka:

The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli and ratings. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions.

Also refer to the paper Tkalčič, Košir, Tasič, ''The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata'' in the Journal on Multimodal User Interfaces, March 2013, Volume 7, Issue 1-2, pp 143-155

=== Excerpt from the dataset ===
<pre>
user_id image_id image_tag genre watching_time wt_m valence_m valence_sd arousal_m arousal_sd dominance_m dominance_sd B_1 B_2 B_3 B_4 B_5 gender age rating
10 6910 Bomber weapon 2614 5307 5,31 2,28 5,62 2,46 5,1 2,46 3,2 2,7 2,9 3,5 2,9 0 18 4
10 9331 Assault violence 2240 3214 2,03 1,35 6,04 2,35 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
10 7052 HairDryer still 2223 2665 4,93 0,81 2,75 1,8 5,82 1,93 3,2 2,7 2,9 3,5 2,9 0 18 1
10 1280 Rat animal 1906 3093 3,66 1,75 4,93 2,01 5,05 2,2 3,2 2,7 2,9 3,5 2,9 0 18 1
10 2394 Medicalworker people 1943 2993 5,76 1,74 3,89 2,26 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
</pre>

== External links ==
* http://slavnik.fe.uni-lj.si/markot/Main/LDOS-PerAff-1
* http://dx.doi.org/10.1007/s12193-012-0107-7

[Category:Dataset]

LDOS PerAff 1

2013-10-24T12:25:39Z

Markotka:

The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli and ratings. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions.

Also refer to the paper Tkalčič, Košir, Tasič, ''The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata'' in the Journal on Multimodal User Interfaces, March 2013, Volume 7, Issue 1-2, pp 143-155

=== Excerpt from the dataset ===
user_id image_id image_tag genre watching_time wt_m valence_m valence_sd arousal_m arousal_sd dominance_m dominance_sd B_1 B_2 B_3 B_4 B_5 gender age rating
10 6910 Bomber weapon 2614 5307 5,31 2,28 5,62 2,46 5,1 2,46 3,2 2,7 2,9 3,5 2,9 0 18 4
10 9331 Assault violence 2240 3214 2,03 1,35 6,04 2,35 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
10 7052 HairDryer still 2223 2665 4,93 0,81 2,75 1,8 5,82 1,93 3,2 2,7 2,9 3,5 2,9 0 18 1
10 1280 Rat animal 1906 3093 3,66 1,75 4,93 2,01 5,05 2,2 3,2 2,7 2,9 3,5 2,9 0 18 1
10 2394 Medicalworker people 1943 2993 5,76 1,74 3,89 2,26 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3

== External links ==
* http://slavnik.fe.uni-lj.si/markot/Main/LDOS-PerAff-1
* http://dx.doi.org/10.1007/s12193-012-0107-7

[Category:Dataset]

LDOS PerAff 1

2013-10-24T12:24:12Z

Markotka:

The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli and ratings. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions.

Also refer to the paper Tkalčič, Košir, Tasič, ''The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata'' in the Journal on Multimodal User Interfaces, March 2013, Volume 7, Issue 1-2, pp 143-155

=== Excerpt from the dataset ===
<nowiki>user_id image_id image_tag genre watching_time wt_m valence_m valence_sd arousal_m arousal_sd dominance_m dominance_sd B_1 B_2 B_3 B_4 B_5 gender age rating
10 6910 Bomber weapon 2614 5307 5,31 2,28 5,62 2,46 5,1 2,46 3,2 2,7 2,9 3,5 2,9 0 18 4
10 9331 Assault violence 2240 3214 2,03 1,35 6,04 2,35 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3
10 7052 HairDryer still 2223 2665 4,93 0,81 2,75 1,8 5,82 1,93 3,2 2,7 2,9 3,5 2,9 0 18 1
10 1280 Rat animal 1906 3093 3,66 1,75 4,93 2,01 5,05 2,2 3,2 2,7 2,9 3,5 2,9 0 18 1
10 2394 Medicalworker people 1943 2993 5,76 1,74 3,89 2,26 0 0 3,2 2,7 2,9 3,5 2,9 0 18 3</nowiki>

== External links ==
* http://slavnik.fe.uni-lj.si/markot/Main/LDOS-PerAff-1
* http://dx.doi.org/10.1007/s12193-012-0107-7

[Category:Dataset]

LDOS PerAff 1

2013-10-24T12:22:16Z

Markotka:

LDOS PerAff 1

2013-10-24T12:16:10Z

Markotka: Created page with "The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli. The corpus is unique for two reasons. First, the emotions are annotated in the v..."

The LDOS-PerAff-1 dataset is a corpus of video clips of users responding to emotional stimuli. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions. The corpus has been compiled for the needs of our research on recommender system.

The dataset is accessible here: [http://slavnik.fe.uni-lj.si/markot/Main/LDOS-PerAff-1 here]

Andrej Kosir

2013-10-21T13:38:26Z

Markotka:

{{Person
|name = Andrej Košir

|affiliation = University of Ljubljana
|country = Slovenia
|website = http://www.ldos.si/eng/index.php?id=01_Members/07_Andrej_Kosir/Index.html

}}

[[Category:People]]

Andrej Košir is associate professor at the University of Ljubljana Faculty of electrical engineering.

Empire 2013

2013-10-21T13:34:33Z

Markotka:

[[Category:Workshop]]
EMPIRE 2013 - 1st workshop on "Emotions and Personality in Personalized Services" http://empire2013.wordpress.com

in conjuction with UMAP 2013 (June 10-14, 2013 Rome, Italy)
http://www.umap2013.org

While a lot of discussion has been made on filtering algorithms, and evaluation measures, few studies have stood to consider the role of emotions and personality in user models and personalized services. Characterizing the user model and the whole user experience with personalized service, by means of affective traits, is an important issue which merits attention from researchers and practitioners in both web technology and human factor fields.

Some questions motivate this workshop:
* Do affective traits (personality, emotions, and mood) influence and determine the acceptance of the personalized suggestions?
* How personality traits should be included in the user model?
* How the personalized services should be adapted to emotions and mood to increase user satisfaction?

== ABSTRACT ==

In the pursuit of increasing the quality of personalized services, researchers started to turn to more user-centric descriptors of content and services in recent years. The advances made in affective computing, especially in automatic emotion detection techniques, paved the way for the exploitation of emotions and personality as descriptors that account for a larger part of variance in user behavior than the generic descriptors (e.g. genre of a multimedia content) used so far.

Emotions, users' responses, can be characterized in different ways. The two most common approaches are (i) the discrete basic emotions (discrete classes, e.g. joy, sadness, fear, disgust, surprise, anger) and (ii) the continuous values, in the valence-arousal-dominance space. The affective computing community has been very active in the past decade and has developed several methods for the automatic non-invasive detection of emotions via several modalities (Zeng et al., 2009).

While emotions can change pretty quickly, personality, on the other hand, describes long-lasting human traits. The most common way of describing personality is the five-factor model (openness, conscientiousness, extraversion, agreeableness and neuroticism).

Emotions and personality in personalized services (e.g., recommender systems) can be exploited in different ways at different stages in the service-usage (e.g. content consumption) chain (Tkalčič et. al, 2011). In the entry stage they can be used as a contextual parameter, as additional information to predict, assist and influence decision-making (Kahneman, 2011) or a way to diversify the personalization via the detection of serendipitous services. In the consumption stage, emotions can be used as additional tags for the characterization of the services, content and users (Jiao and Pantid, 2011), opening new research areas for modeling services and content with different lengths. Finally, emotions can be exploited also for the non-invasive acquisition of the implicit user feedback as well as for novel evaluation metrics.

So far, research on emotions and personality in personalized services has been carried out in a scattered fashion. The goal of this workshop is to provide a venue for researchers to present their work, discuss it and benefit from the interaction.

== TOPICS ==

* Affective modeling
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction...)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Models based on personality
* Datasets for affective modeling (Collecting, Available)
* Personality traits acquisition (explicit vs. implicit)
* Assessing personality traits implicitly from users’ activities/ratings/behavior
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits? Should they be?
* Personality and users’ tasks/goals
* Do personality traits influence users’ goals?
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Recognizing triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors

== SUBMISSION INSTRUCTIONS ==

Two kinds of submissions are accepted:(i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2013

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a centralized CEUR-WS volume of workshop papers and conference posters.

Further information can be found on the workshop's web page http://empire2013.wordpress.com

== IMPORTANT DATES ==

* April, 1, 2013 Paper submission deadline
* May, 1, 2013 Notification of acceptance
* To Be Announced Workshop day

== ORGANIZING COMMITTEE ==

* Marko Tkalčič, University of Ljubljana, Slovenia
* Berardina De Carolis, University of Bari Aldo Moro, Italy
* Marco de Gemmis, University of Bari Aldo Moro, Italy
* Ante Odić, University of Ljubljana, Slovenia
* Andrej Košir, University of Ljubljana, Slovenia

== PROGRAM COMMITTEE (to be extended) ==

* Alessandro Vinciarelli, University of Glasgow
* Elisabeth Andre, Augsburg University
* Floriana Grasso, Univ. Liverpool
* Francesco Ricci, Free University of Bozen-Bolzano
* Gustavo Gonzalez, http://goo.gl/tjDx0
* Ioannis Arapakis, Yahoo! Barcelona
* Jennifer Golbeck, University of Maryland
* Judith Masthoff, University of Aberdeen
* Li Chen, Hong Kong Baptist University
* Man-Kwan Shan, National Chengchi University, Department of Computer Science
* Marius Kaminskas, Free University of Bolzano
* Martijn Willemsen, Eindhoven University of Technology, Netherlands
* Markus Zanker, University Klagenfurt, Austria
* Michal Kosinski, Microsoft
* Mohammad Soleymani, Univ. Geneva/Imperial college
* Neal Lathia, Cambridge University
* Rong Hu , EPFL

Andrej Kosir

2013-10-21T13:32:17Z

Markotka: Created page with "Andrej Košir is associate professor at the University of Ljubljana Faculty of electrical engineering."

Andrej Košir is associate professor at the University of Ljubljana Faculty of electrical engineering.

Marko Tkalcic

2013-10-21T07:56:07Z

Markotka:

{{Person
|name = Marko Tkalčič

|affiliation = Johannes Kepler University, Linz
|country = Austria
|website = http://markotkalcic.wordpress.com/

}}

'''Marko Tkalčič''' is a post-doctoral research scientist at the [http://www.cp.jku.at/ Department of Computational Perception] at the Johannes Kepler University in Linz, Austria.

Before that, he was a research scientist at the [http://www.ldos.si LDOS] group at the University of Ljubljana Faculty of electrical engineering.

His research focuses on the usage of emotions and personality in recommender systems.

[[Category: People]]

Marko Tkalcic

2013-10-21T07:53:51Z

Markotka:

{{Person
|name = Marko Tkalčič

|affiliation = University of Ljubljana
|country = Slovenia
|website = http://slavnik.fe.uni-lj.si/markot/

}}

'''Marko Tkalčič''' is a post-doctoral research scientist at the [http://www.cp.jku.at/ Department of Computational Perception] at the Johannes Kepler University in Linz, Austria.

Before that, he was a research scientist at the [http://www.ldos.si LDOS] group at the University of Ljubljana Faculty of electrical engineering. His research focuses on the usage of emotions in recommender systems.

[[Category: People]]

Empire 2013

2013-02-03T17:35:40Z

Markotka: /* PROGRAM COMMITTEE (to be extended) */

EMPIRE 2013 - 1st workshop on "Emotions and Personality in Personalized Services" http://empire2013.wordpress.com

in conjuction with UMAP 2013 (June 10-14, 2013 Rome, Italy)
http://www.umap2013.org

While a lot of discussion has been made on filtering algorithms, and evaluation measures, few studies have stood to consider the role of emotions and personality in user models and personalized services. Characterizing the user model and the whole user experience with personalized service, by means of affective traits, is an important issue which merits attention from researchers and practitioners in both web technology and human factor fields.

Some questions motivate this workshop:
* Do affective traits (personality, emotions, and mood) influence and determine the acceptance of the personalized suggestions?
* How personality traits should be included in the user model?
* How the personalized services should be adapted to emotions and mood to increase user satisfaction?

== ABSTRACT ==

In the pursuit of increasing the quality of personalized services, researchers started to turn to more user-centric descriptors of content and services in recent years. The advances made in affective computing, especially in automatic emotion detection techniques, paved the way for the exploitation of emotions and personality as descriptors that account for a larger part of variance in user behavior than the generic descriptors (e.g. genre of a multimedia content) used so far.

Emotions, users' responses, can be characterized in different ways. The two most common approaches are (i) the discrete basic emotions (discrete classes, e.g. joy, sadness, fear, disgust, surprise, anger) and (ii) the continuous values, in the valence-arousal-dominance space. The affective computing community has been very active in the past decade and has developed several methods for the automatic non-invasive detection of emotions via several modalities (Zeng et al., 2009).

While emotions can change pretty quickly, personality, on the other hand, describes long-lasting human traits. The most common way of describing personality is the five-factor model (openness, conscientiousness, extraversion, agreeableness and neuroticism).

Emotions and personality in personalized services (e.g., recommender systems) can be exploited in different ways at different stages in the service-usage (e.g. content consumption) chain (Tkalčič et. al, 2011). In the entry stage they can be used as a contextual parameter, as additional information to predict, assist and influence decision-making (Kahneman, 2011) or a way to diversify the personalization via the detection of serendipitous services. In the consumption stage, emotions can be used as additional tags for the characterization of the services, content and users (Jiao and Pantid, 2011), opening new research areas for modeling services and content with different lengths. Finally, emotions can be exploited also for the non-invasive acquisition of the implicit user feedback as well as for novel evaluation metrics.

So far, research on emotions and personality in personalized services has been carried out in a scattered fashion. The goal of this workshop is to provide a venue for researchers to present their work, discuss it and benefit from the interaction.

== TOPICS ==

* Affective modeling
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction...)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Models based on personality
* Datasets for affective modeling (Collecting, Available)
* Personality traits acquisition (explicit vs. implicit)
* Assessing personality traits implicitly from users’ activities/ratings/behavior
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits? Should they be?
* Personality and users’ tasks/goals
* Do personality traits influence users’ goals?
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Recognizing triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors

== SUBMISSION INSTRUCTIONS ==

Two kinds of submissions are accepted:(i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2013

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a centralized CEUR-WS volume of workshop papers and conference posters.

Further information can be found on the workshop's web page http://empire2013.wordpress.com

== IMPORTANT DATES ==

* April, 1, 2013 Paper submission deadline
* May, 1, 2013 Notification of acceptance
* To Be Announced Workshop day

== ORGANIZING COMMITTEE ==

* Marko Tkalčič, University of Ljubljana, Slovenia
* Berardina De Carolis, University of Bari Aldo Moro, Italy
* Marco de Gemmis, University of Bari Aldo Moro, Italy
* Ante Odić, University of Ljubljana, Slovenia
* Andrej Košir, University of Ljubljana, Slovenia

== PROGRAM COMMITTEE (to be extended) ==

* Alessandro Vinciarelli, University of Glasgow
* Elisabeth Andre, Augsburg University
* Floriana Grasso, Univ. Liverpool
* Francesco Ricci, Free University of Bozen-Bolzano
* Gustavo Gonzalez, http://goo.gl/tjDx0
* Ioannis Arapakis, Yahoo! Barcelona
* Jennifer Golbeck, University of Maryland
* Judith Masthoff, University of Aberdeen
* Li Chen, Hong Kong Baptist University
* Man-Kwan Shan, National Chengchi University, Department of Computer Science
* Marius Kaminskas, Free University of Bolzano
* Martijn Willemsen, Eindhoven University of Technology, Netherlands
* Markus Zanker, University Klagenfurt, Austria
* Michal Kosinski, Microsoft
* Mohammad Soleymani, Univ. Geneva/Imperial college
* Neal Lathia, Cambridge University
* Rong Hu , EPFL

Empire 2013

2013-02-03T17:35:06Z

Markotka: /* ORGANIZING COMMITTEE */

EMPIRE 2013 - 1st workshop on "Emotions and Personality in Personalized Services" http://empire2013.wordpress.com

in conjuction with UMAP 2013 (June 10-14, 2013 Rome, Italy)
http://www.umap2013.org

While a lot of discussion has been made on filtering algorithms, and evaluation measures, few studies have stood to consider the role of emotions and personality in user models and personalized services. Characterizing the user model and the whole user experience with personalized service, by means of affective traits, is an important issue which merits attention from researchers and practitioners in both web technology and human factor fields.

Some questions motivate this workshop:
* Do affective traits (personality, emotions, and mood) influence and determine the acceptance of the personalized suggestions?
* How personality traits should be included in the user model?
* How the personalized services should be adapted to emotions and mood to increase user satisfaction?

== ABSTRACT ==

In the pursuit of increasing the quality of personalized services, researchers started to turn to more user-centric descriptors of content and services in recent years. The advances made in affective computing, especially in automatic emotion detection techniques, paved the way for the exploitation of emotions and personality as descriptors that account for a larger part of variance in user behavior than the generic descriptors (e.g. genre of a multimedia content) used so far.

Emotions, users' responses, can be characterized in different ways. The two most common approaches are (i) the discrete basic emotions (discrete classes, e.g. joy, sadness, fear, disgust, surprise, anger) and (ii) the continuous values, in the valence-arousal-dominance space. The affective computing community has been very active in the past decade and has developed several methods for the automatic non-invasive detection of emotions via several modalities (Zeng et al., 2009).

While emotions can change pretty quickly, personality, on the other hand, describes long-lasting human traits. The most common way of describing personality is the five-factor model (openness, conscientiousness, extraversion, agreeableness and neuroticism).

Emotions and personality in personalized services (e.g., recommender systems) can be exploited in different ways at different stages in the service-usage (e.g. content consumption) chain (Tkalčič et. al, 2011). In the entry stage they can be used as a contextual parameter, as additional information to predict, assist and influence decision-making (Kahneman, 2011) or a way to diversify the personalization via the detection of serendipitous services. In the consumption stage, emotions can be used as additional tags for the characterization of the services, content and users (Jiao and Pantid, 2011), opening new research areas for modeling services and content with different lengths. Finally, emotions can be exploited also for the non-invasive acquisition of the implicit user feedback as well as for novel evaluation metrics.

So far, research on emotions and personality in personalized services has been carried out in a scattered fashion. The goal of this workshop is to provide a venue for researchers to present their work, discuss it and benefit from the interaction.

== TOPICS ==

* Affective modeling
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction...)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Models based on personality
* Datasets for affective modeling (Collecting, Available)
* Personality traits acquisition (explicit vs. implicit)
* Assessing personality traits implicitly from users’ activities/ratings/behavior
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits? Should they be?
* Personality and users’ tasks/goals
* Do personality traits influence users’ goals?
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Recognizing triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors

== SUBMISSION INSTRUCTIONS ==

Two kinds of submissions are accepted:(i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2013

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a centralized CEUR-WS volume of workshop papers and conference posters.

Further information can be found on the workshop's web page http://empire2013.wordpress.com

== IMPORTANT DATES ==

* April, 1, 2013 Paper submission deadline
* May, 1, 2013 Notification of acceptance
* To Be Announced Workshop day

== ORGANIZING COMMITTEE ==

* Marko Tkalčič, University of Ljubljana, Slovenia
* Berardina De Carolis, University of Bari Aldo Moro, Italy
* Marco de Gemmis, University of Bari Aldo Moro, Italy
* Ante Odić, University of Ljubljana, Slovenia
* Andrej Košir, University of Ljubljana, Slovenia

== PROGRAM COMMITTEE (to be extended) ==

Alessandro Vinciarelli, University of Glasgow
Elisabeth Andre, Augsburg University
Floriana Grasso, Univ. Liverpool
Francesco Ricci, Free University of Bozen-Bolzano
Gustavo Gonzalez, http://goo.gl/tjDx0
Ioannis Arapakis, Yahoo! Barcelona
Jennifer Golbeck, University of Maryland
Judith Masthoff, University of Aberdeen
Li Chen, Hong Kong Baptist University
Man-Kwan Shan, National Chengchi University, Department of Computer Science
Marius Kaminskas, Free University of Bolzano
Martijn Willemsen, Eindhoven University of Technology, Netherlands
Markus Zanker, University Klagenfurt, Austria
Michal Kosinski, Microsoft
Mohammad Soleymani, Univ. Geneva/Imperial college
Neal Lathia, Cambridge University
Rong Hu , EPFL

Empire 2013

2013-02-03T17:34:40Z

Markotka: /* IMPORTANT DATES */

EMPIRE 2013 - 1st workshop on "Emotions and Personality in Personalized Services" http://empire2013.wordpress.com

in conjuction with UMAP 2013 (June 10-14, 2013 Rome, Italy)
http://www.umap2013.org

While a lot of discussion has been made on filtering algorithms, and evaluation measures, few studies have stood to consider the role of emotions and personality in user models and personalized services. Characterizing the user model and the whole user experience with personalized service, by means of affective traits, is an important issue which merits attention from researchers and practitioners in both web technology and human factor fields.

Some questions motivate this workshop:
* Do affective traits (personality, emotions, and mood) influence and determine the acceptance of the personalized suggestions?
* How personality traits should be included in the user model?
* How the personalized services should be adapted to emotions and mood to increase user satisfaction?

== ABSTRACT ==

In the pursuit of increasing the quality of personalized services, researchers started to turn to more user-centric descriptors of content and services in recent years. The advances made in affective computing, especially in automatic emotion detection techniques, paved the way for the exploitation of emotions and personality as descriptors that account for a larger part of variance in user behavior than the generic descriptors (e.g. genre of a multimedia content) used so far.

Emotions, users' responses, can be characterized in different ways. The two most common approaches are (i) the discrete basic emotions (discrete classes, e.g. joy, sadness, fear, disgust, surprise, anger) and (ii) the continuous values, in the valence-arousal-dominance space. The affective computing community has been very active in the past decade and has developed several methods for the automatic non-invasive detection of emotions via several modalities (Zeng et al., 2009).

While emotions can change pretty quickly, personality, on the other hand, describes long-lasting human traits. The most common way of describing personality is the five-factor model (openness, conscientiousness, extraversion, agreeableness and neuroticism).

Emotions and personality in personalized services (e.g., recommender systems) can be exploited in different ways at different stages in the service-usage (e.g. content consumption) chain (Tkalčič et. al, 2011). In the entry stage they can be used as a contextual parameter, as additional information to predict, assist and influence decision-making (Kahneman, 2011) or a way to diversify the personalization via the detection of serendipitous services. In the consumption stage, emotions can be used as additional tags for the characterization of the services, content and users (Jiao and Pantid, 2011), opening new research areas for modeling services and content with different lengths. Finally, emotions can be exploited also for the non-invasive acquisition of the implicit user feedback as well as for novel evaluation metrics.

So far, research on emotions and personality in personalized services has been carried out in a scattered fashion. The goal of this workshop is to provide a venue for researchers to present their work, discuss it and benefit from the interaction.

== TOPICS ==

* Affective modeling
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction...)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Models based on personality
* Datasets for affective modeling (Collecting, Available)
* Personality traits acquisition (explicit vs. implicit)
* Assessing personality traits implicitly from users’ activities/ratings/behavior
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits? Should they be?
* Personality and users’ tasks/goals
* Do personality traits influence users’ goals?
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Recognizing triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors

== SUBMISSION INSTRUCTIONS ==

Two kinds of submissions are accepted:(i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2013

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a centralized CEUR-WS volume of workshop papers and conference posters.

Further information can be found on the workshop's web page http://empire2013.wordpress.com

== IMPORTANT DATES ==

* April, 1, 2013 Paper submission deadline
* May, 1, 2013 Notification of acceptance
* To Be Announced Workshop day

== ORGANIZING COMMITTEE ==

Marko Tkalčič, University of Ljubljana, Slovenia
Berardina De Carolis, University of Bari Aldo Moro, Italy
Marco de Gemmis, University of Bari Aldo Moro, Italy
Ante Odić, University of Ljubljana, Slovenia
Andrej Košir, University of Ljubljana, Slovenia

== PROGRAM COMMITTEE (to be extended) ==

Alessandro Vinciarelli, University of Glasgow
Elisabeth Andre, Augsburg University
Floriana Grasso, Univ. Liverpool
Francesco Ricci, Free University of Bozen-Bolzano
Gustavo Gonzalez, http://goo.gl/tjDx0
Ioannis Arapakis, Yahoo! Barcelona
Jennifer Golbeck, University of Maryland
Judith Masthoff, University of Aberdeen
Li Chen, Hong Kong Baptist University
Man-Kwan Shan, National Chengchi University, Department of Computer Science
Marius Kaminskas, Free University of Bolzano
Martijn Willemsen, Eindhoven University of Technology, Netherlands
Markus Zanker, University Klagenfurt, Austria
Michal Kosinski, Microsoft
Mohammad Soleymani, Univ. Geneva/Imperial college
Neal Lathia, Cambridge University
Rong Hu , EPFL

Empire 2013

2013-02-03T17:33:45Z

Markotka: Created page with "EMPIRE 2013 - 1st workshop on "Emotions and Personality in Personalized Services" http://empire2013.wordpress.com in conjuction with UMAP 2013 (June 10-14, 2013 Rome, Italy) ..."

EMPIRE 2013 - 1st workshop on "Emotions and Personality in Personalized Services" http://empire2013.wordpress.com

in conjuction with UMAP 2013 (June 10-14, 2013 Rome, Italy)
http://www.umap2013.org

While a lot of discussion has been made on filtering algorithms, and evaluation measures, few studies have stood to consider the role of emotions and personality in user models and personalized services. Characterizing the user model and the whole user experience with personalized service, by means of affective traits, is an important issue which merits attention from researchers and practitioners in both web technology and human factor fields.

Some questions motivate this workshop:
* Do affective traits (personality, emotions, and mood) influence and determine the acceptance of the personalized suggestions?
* How personality traits should be included in the user model?
* How the personalized services should be adapted to emotions and mood to increase user satisfaction?

== ABSTRACT ==

In the pursuit of increasing the quality of personalized services, researchers started to turn to more user-centric descriptors of content and services in recent years. The advances made in affective computing, especially in automatic emotion detection techniques, paved the way for the exploitation of emotions and personality as descriptors that account for a larger part of variance in user behavior than the generic descriptors (e.g. genre of a multimedia content) used so far.

Emotions, users' responses, can be characterized in different ways. The two most common approaches are (i) the discrete basic emotions (discrete classes, e.g. joy, sadness, fear, disgust, surprise, anger) and (ii) the continuous values, in the valence-arousal-dominance space. The affective computing community has been very active in the past decade and has developed several methods for the automatic non-invasive detection of emotions via several modalities (Zeng et al., 2009).

While emotions can change pretty quickly, personality, on the other hand, describes long-lasting human traits. The most common way of describing personality is the five-factor model (openness, conscientiousness, extraversion, agreeableness and neuroticism).

Emotions and personality in personalized services (e.g., recommender systems) can be exploited in different ways at different stages in the service-usage (e.g. content consumption) chain (Tkalčič et. al, 2011). In the entry stage they can be used as a contextual parameter, as additional information to predict, assist and influence decision-making (Kahneman, 2011) or a way to diversify the personalization via the detection of serendipitous services. In the consumption stage, emotions can be used as additional tags for the characterization of the services, content and users (Jiao and Pantid, 2011), opening new research areas for modeling services and content with different lengths. Finally, emotions can be exploited also for the non-invasive acquisition of the implicit user feedback as well as for novel evaluation metrics.

So far, research on emotions and personality in personalized services has been carried out in a scattered fashion. The goal of this workshop is to provide a venue for researchers to present their work, discuss it and benefit from the interaction.

== TOPICS ==

* Affective modeling
* Emotions as context
* Emotions in the decision-making process for recommender systems
* Role of personality on user similarities
* Emotion detection in recommended content consumption
* Emotion detection as non-invasive feedback
* Affective tagging of multimedia content and services
* Emotion-based evaluation metrics (satisfaction...)
* Lifestyle recommender systems
* Personality and mood for group decision making
* Incorporating personality and emotions in user models
* Models based on personality
* Datasets for affective modeling (Collecting, Available)
* Personality traits acquisition (explicit vs. implicit)
* Assessing personality traits implicitly from users’ activities/ratings/behavior
* Personality and interfaces/control/bubble-control
* Could interfaces/control/bubble-control be personalized based on personality traits? Should they be?
* Personality and users’ tasks/goals
* Do personality traits influence users’ goals?
* Social signal processing for personalized services
* Strategies for modeling emotions and personality
* Recognizing triggers and causes of emotion
* Theories about the relationship between reasoning and affect, between decision-making and affect
* Methods for evaluating the utility of adaptation to affective factors

== SUBMISSION INSTRUCTIONS ==

Two kinds of submissions are accepted:(i) full papers (up to 12 pages) and (ii) short papers (up to 6 pages). Submissions should be made through the EasyChair conference system:

https://www.easychair.org/conferences/?conf=empire2013

and must adhere to the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). All the submissions will be peer-reviewed. The accepted papers will be published in a centralized CEUR-WS volume of workshop papers and conference posters.

Further information can be found on the workshop's web page http://empire2013.wordpress.com

== IMPORTANT DATES ==

April, 1, 2013 Paper submission deadline
May, 1, 2013 Notification of acceptance
To Be Announced Workshop day

== ORGANIZING COMMITTEE ==

Marko Tkalčič, University of Ljubljana, Slovenia
Berardina De Carolis, University of Bari Aldo Moro, Italy
Marco de Gemmis, University of Bari Aldo Moro, Italy
Ante Odić, University of Ljubljana, Slovenia
Andrej Košir, University of Ljubljana, Slovenia

== PROGRAM COMMITTEE (to be extended) ==

Alessandro Vinciarelli, University of Glasgow
Elisabeth Andre, Augsburg University
Floriana Grasso, Univ. Liverpool
Francesco Ricci, Free University of Bozen-Bolzano
Gustavo Gonzalez, http://goo.gl/tjDx0
Ioannis Arapakis, Yahoo! Barcelona
Jennifer Golbeck, University of Maryland
Judith Masthoff, University of Aberdeen
Li Chen, Hong Kong Baptist University
Man-Kwan Shan, National Chengchi University, Department of Computer Science
Marius Kaminskas, Free University of Bolzano
Martijn Willemsen, Eindhoven University of Technology, Netherlands
Markus Zanker, University Klagenfurt, Austria
Michal Kosinski, Microsoft
Mohammad Soleymani, Univ. Geneva/Imperial college
Neal Lathia, Cambridge University
Rong Hu , EPFL

Marko Tkalcic

2012-05-04T12:11:19Z

Markotka:

{{Person
|name = Marko Tkalčič

|affiliation = University of Ljubljana
|country = Slovenia
|website = http://slavnik.fe.uni-lj.si/markot/

}}

'''Marko Tkalčič''' is a research scientist at the [http://www.ldos.si LDOS] group at the University of Ljubljana Faculty of electrical engineering. His research focuses on the usage of emotions in recommender systems.

[[Category: People]]

Marko Tkalcic

2012-05-04T12:08:48Z

Markotka:

{{Person
|name = Marko Tkalčič

|affiliation = University of Ljubljana
|country = Slovenia
|website = http://slavnik.fe.uni-lj.si/markot/

}}

'''Marko Tkalčič''' is a research scientist at the [http://www.ldos.si LDOS] group at the University of Ljubljana Faculty of electrical engineering

[[Category: People]]

Marko Tkalcic

2012-05-04T12:07:15Z

Markotka: Created page with "{{Person |name = Marko Tkalčič  |affiliation = University of Ljubljana |country = Slovenia |website = http://slavnik.fe.uni-lj.si/mar..."

{{Person
|name = Marko Tkalčič

|affiliation = University of Ljubljana
|country = Slovenia
|website = http://slavnik.fe.uni-lj.si/markot/

}}

'''Marko Tkalčič''' is a research scientist at the [[http://www.ldos.si|LDOS]] group at the University of Ljubljana Faculty of electrical engineering

[[Category: People]]

List of recommender system dissertations

2012-01-06T10:28:22Z

Markotka: /* 2011 */

Recommender systems-related dissertations by year.

=== 2011 ===
* [http://slavnik.fe.uni-lj.si/markot/uploads/Main/2010_tkalcic_phd.pdf Recognition and usage of emotive parameters in recommender systems] - [[Marko Tkalčič]]
* [http://www.inf.unibz.it/~lbaltrunas/doc/linas_phd_thesis.pdf Context-Aware Collaborative Filtering Recommender Systems] - [[Linas Baltrunas]]
* [http://www.aka-verlag.com/de/detail?ean=978-3-89838-332-5 Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems] - [[Robert Jäschke]]
* [http://benfields.net/bfields_thesis.pdf Contextualize Your Listening: The Playlist as Recommendation Engine] - [[Ben Fields]]
* [http://users.cecs.anu.edu.au/~sguo/thesis.pdf Bayesian Recommender Systems: Models and Algorithms] - [[Shengbo Guo]]

=== 2010 ===
* [http://www.cp.jku.at/people/seyerlehner/supervised/seyerlehner_phd.pdf Content-Based Music Recommender Systems: Beyond simple Frame-Level Audio Similarity] - [[Klaus Seyerlehner]]
* [http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-16897-0 Context-Aware Ranking with Factorization Models] - [[Steffen Rendle]]
* [http://www.cs.ucl.ac.uk/staff/n.lathia/thesis.html Evaluating Collaborative Filtering Over Time] - [[Neal Lathia]]
* [http://opus.kobv.de/tuberlin/volltexte/2010/2695/pdf/wetzker_robert.pdf Graph-Based Recommendation in Broad Folksonomies] - [[Robert Wetzker]]

=== 2009 ===
* [http://www.abdn.ac.uk/~csc284/Nava%20Tintarev_PhD_Thesis_%282010%29.pdf Explaining recommendations] - [[Nava Tintarev]]
* [http://www.omikk.bme.hu/collections/phd/Villamosmernoki_es_Informatikai_Kar/2010/Pilaszy_Istvan/ertekezes.pdf Factorization-Based Large Scale Recommendation Algorithms] - [[István Pilászy]]
* [http://itlab.dbit.dk/~toine/?page_id=6 Recommender Systems for Social Bookmarking] - [[Toine Bogers]]
* [http://svn.egovmon.no/svn/phdgoodwin/thesis/referenceexample/MMR_thesis_afterDefense.pdf Towards Efficient Music Similarity Search, Ranking, and Recommendation] - [[Maria Magdalena Ruxanda]]

=== 2008 ===
* [http://arantxa.ii.uam.es/~cantador/doc/2008/thesis08.zip Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-based Approach] - [[Iván Cantador]]
* [http://www.cs.umass.edu/~marlin/research/phd_thesis/marlin-phd-thesis.pdf Missing Data Problems in Machine Learning] - [[Benjamin Marlin]]
* [http://mtg.upf.edu/node/1217 Music Recommendation and Discovery in the Long Tail] - [[Òscar Celma]]
* [http://tomheath.com/thesis/html Information-seeking on the Web with Trusted Social Networks – from Theory to Systems] - [[Tom Heath]]
* [http://winnie.kuis.kyoto-u.ac.jp/members/yoshii/d-thesis-yoshii.pdf Studies on Hybrid Music Recommendation Using Timbral and Rhythmic Features] - [[Kazuyoshi Yoshii]]
* [http://hci.epfl.ch/members/lichen/EPFL_TH4140.pdf User Decision Improvement and Trust Building in Product Recommender Systems] - [[Li Chen]]

=== 2007 ===
* [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2714&rep=rep1&type=pdf Building Trustworthy Recommender Systems] - [[Sheng Zhang]]

=== 2006 ===
* [http://www-users.cs.umn.edu/~mcnee/mcnee-thesis-preprint.pdf Meeting User Information Needs in Recommender Systems] - [[Sean McNee]]

=== 2005 ===
* [https://doc.telin.nl/dsweb/Get/Document-56873 Supporting People In Finding Information: Hybrid Recommender Systems and Goal-Based Structuring] - [[Mark van Setten]]
* [http://www.freidok.uni-freiburg.de/volltexte/1804/pdf/Thesis.pdf Towards Decentralized Recommender Systems] - [[Cai-Nicolas Ziegler]]

=== 2004 ===

=== 2003 ===
* [http://knuth.luther.edu/~bmiller/Papers/thesis.pdf Toward a Personal Recommender System] - [[Bradley N. Miller]]
* [http://eia.udg.es/~mmontane/montaner-thesis03.pdf Collaborative recommender agents based on case-based reasoning and trust] - [[Miquel Montaner]]

=== 2002 ===

=== 2001 ===
* [http://www.patrickbaudisch.com/publications/2001-Baudisch-Dissertation-DynamicInformationFiltering.pdf Dynamic Information Filtering] - [[Patrick Baudisch]]
* [http://www-users.cs.umn.edu/~sarwar/thesis.ps Sparsity, scalability, and distribution in recommender systems] - [[Badrul Munir Sarwar]]

== External links ==
* [http://pampalk.at/mir-phds/ PhD Theses and Doctoral Dissertations Related to Music Information Retrieval]

[[Category: List|Dissertation]]